LangChain is the plumbing. One interface for chat models, tools, prompts and message types, so swapping Groq for OpenAI is a string change rather than a rewrite. Most agent frameworks, LangGraph included, are built on top of it.
Reach for it when
- You want to call several providers without rewriting code
- You need tools, structured output and message handling as a foundation
- You are learning agents and want to understand the layer underneath
Look elsewhere when
- You only ever call one provider. Use that provider's SDK
- You need complex branching and loops. That is LangGraph
What the lessons will cover
- 01Install and first model call
- 02Messages and roles
- 03Prompt templates
- 04Tools with @tool
- 05Structured output
- 06Streaming and batching
- 07Swapping providers
- 08create_agent, the short path
Others in graph and workflow
Common questions
Is LangChain free to use?
LangChain is open source and free to run yourself. You still pay whichever model provider you point it at, and this tutorial is free with no signup.
Do I need to know Python to use LangChain?
Basic Python is enough. Functions, dictionaries and imports cover most of what LangChain asks of you.
When should I not use LangChain?
You only ever call one provider. Use that provider's SDK. You need complex branching and loops. That is LangGraph.
Lessons for LangChain are being written. Meanwhile the LangGraph tutorial covers the same ground: state, tools, loops, memory and human approval. Most of it carries straight over.
Start the LangGraph tutorial →